A Tech Revolution for the Homeless - AEI Tech Revolution for the Homeless aking Big Data ......

KEVIN CORINTHAPRIL 2016

A M E R I C A N E N T E R P R I S E I N S T I T U T E

A Tech Revolution forthe Homeless

TakingBig Datato theStreets

Provide free smartphones and service plans to homeless individuals who sleep on the streets.

The Proposal

Ask individuals to provide regular check-ins on their sleeping location, health, service use, and happiness.

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180$190

Federal City ShelterCovenant HouseThe Haven

Central Union Mission

Use current location

Other location

Daily Update

Updated Just Now

Where are you sleeping tonight?

Choose from recent shelters

Search or enter an address

DoneCancel

Weekly Update

Updated Just Now

DoneCancel

2122

181920 hrs

How many hours did you work?

How much did you earn?

Data plan maintained in exchange for check-ins.

App interface mock-ups

Use randomized controlled trials and data analysis to test innovative ideas and learn what works.

A B

Use predictive analytics to recommend concrete actions for the homeless and evaluate service providers.

YOUR PROGRAM RECOMMENDATIONS

GIVE OUT SMARTPHONES

COLLECT DATA

TEST IDEAS EMPOWER USERS

TRANSFORM POLICYCreate personalized interventions for homeless individuals that respond in real time to evolving evidence and changing circumstances.

• Data Scientists• Experts• Homeless Individuals

REAL-TIME DATA

PERSONALIZED INTERVENTIONS

HOMELESS INNOVATION CENTER

A Tech Revolutionfor the Homeless

KEVIN CORINTH

APRIL 2016

A M E R I C A N E N T E R P R I S E I N S T I T U T E

Taking Big Data to the Streets

© 2016 by the American Enterprise Institute for Public Policy Research. All rights reserved. The American Enterprise Institute for Public Policy Research (AEI) is a nonpartisan, nonprofit, 501(c)(3) educational organization and does not take institutional positions on any issues. The views expressed here are those of the author(s).

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Table of Contents

Executive Summary ...................................................................................................................................... 1

Mobile Phones and Homelessness ................................................................................................................ 3

Collecting Data via Smartphones ................................................................................................................. 5

Research Benefits .......................................................................................................................................... 8

Smarter Decisions and Accountability ........................................................................................................ 11

Homeless Innovation Center ...................................................................................................................... 13

The Pilot ...................................................................................................................................................... 15

Conclusion .................................................................................................................................................. 17

Appendix A.................................................................................................................................................. 18

Appendix B .................................................................................................................................................. 19

Acknowledgements ...................................................................................................................................... 24

Notes ........................................................................................................................................................... 25

1

Executive Summary

Homelessness has become an intractable problem of modern life. Across the United States, more

than 500,000 people are homeless on a single night.1

Major cities such as Los Angeles, New York, and Seattle report unprecedented numbers of people sleeping on the street. This is not due to a lack of societal effort. The federal government alone spends more than $5 billion per year on homeless assistance, an increase of 36 per-cent over the past five years.2 Local communities began declaring 10-year plans to end homelessness after the turn of the new millennium. Yet more than a decade later, an end to homelessness is nowhere in sight. What is needed is a revolution in how we confront the issue.

Imagine harnessing the latest innovations in tech-nology and data science to serve the homeless sleeping on our streets. I propose equipping homeless individu-als with free smartphones and service plans in exchange for providing daily information on themselves through a specialized app—including their sleeping locations, use of services, and personal outcomes. The possibil-ities could transform how we understand and con-front homelessness. With the ability to track individual outcomes regardless of where people sleep, we could answer important research questions. We could test new ideas using randomized controlled trials, sending interventions directly to individuals via their smart-phones and tracking the results in real time. We could empower homeless individuals with data-powered pre-diction tools that recommend concrete actions, and we could use the data we collect to hold service providers accountable for helping individuals achieve sustained positive outcomes. Policy itself could be revolutionized with the capability to deliver packages of interventions that are individualized, evidence-based, and dynam-ically updated based on people’s actual experiences. Ultimately, a tech revolution for the homeless would unleash the same innovation that has transformed

our lives—from the ways we interact, to how we buy things, to how we get around—to help transform the lives of the homeless sleeping on our streets.

One important benefit of this proposal is the abil-ity to answer a wide range of important research ques-tions. Using data collected from smartphones, we could quantify the health effects of sleeping in specific unshel-tered and sheltered locations. We could determine how street sweeps, homeless encampment closures, and arrests affect the locational decisions and well-being of homeless individuals. We could discover which factors are associated with people exiting the streets, connect-ing with services, and remaining in housing. Provid-ing high-quality answers to these and other questions would advance our knowledge of the homeless and how best to serve them.

Smartphones can also provide a platform for a wide range of randomized controlled trials, the gold standard in social science for discovering what works. Interven-tions could be delivered directly via smartphones, and results could be tracked in real time. For example, cash incentives could be offered via online banking accounts for positive actions such as checking in with a ser-vice provider or applying for a job. Randomly varying the amount of cash provided to different individuals would allow us to determine the causal impact of cash incentives on various outcomes such as health, hous-ing stability, and employment. A whole range of other interventions could be evaluated as well, from informa-tion about services to in-kind benefits such as sleeping bags or Uber credits.

In addition to expanding research opportuni-ties, data collected from smartphones can be used to improve information and make service providers more accountable. Statistical algorithms could pre-dict individual outcomes and recommend particu-lar services based on a person’s characteristics, history

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A TECH REVOLUTION FOR THE HOMELESS

and preferences, empowering homeless individuals to make more informed decisions. Service providers could be evaluated and held accountable by funders, based on data collected from the app demonstrating their effectiveness.

Finally, policy can be transformed to provide per-sonalized packages of interventions to individuals that reflect changing circumstances and evolving evidence. A Homeless Innovation Center could be tasked with the dual roles of building evidence for what works and implementing personalized interventions. Provided broad goals by its funding sources, the center would devise a research agenda that identifies promising inter-ventions and test them with ongoing data analysis and randomized controlled trials. The center would create a unique package of interventions for each specific indi-vidual based on his unique history and circumstances. As evidence is gathered on how each intervention per-forms, the package of interventions could be continu-ally refined and improved in real time.

The capability of smartphones to revolution-ize homelessness research, the homelessness system, and homelessness policy relies on the untested but

imperative assumption that homeless individuals will upload accurate, uninterrupted data via smartphones. A pilot project should be undertaken to test this assumption. A data collection app, smartphone distri-bution procedures, and other mechanisms for ensuring reliable data collection can be developed and refined. A pilot could confront challenges such as smartphone loss, theft, and sale. At the successful conclusion of the pilot, smartphone distribution and data collection can be scaled up to entire cities, and broader system- and policy-wide transformations can be introduced.

The rest of this proposal proceeds by describing the full project and its potential benefits in detail: Section 2 summarizes the state of our knowledge about mobile phones and homelessness. Section 3 describes how data would be collected with smartphones and the major challenges in doing so. Section 4 discusses research advancements made possible by the proposal. Section 5 describes the benefits for homeless decision making and service provider accountability. Section 6 describes the proposed Homeless Innovation Center. Section 7 discusses the pilot and potential funding mechanisms. Section 8 concludes.

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Mobile Phones and Homelessness

With mobile phones becoming more important for connecting to society, increasing attention is

being paid to their use among the homeless population. Recent research is finding that significant fractions of homeless individuals use mobile phones, and new pilot initiatives are providing smartphones to specific groups. This section briefly summarizes the recent research and initiatives related to mobile phones and homelessness, as well as what lessons can be drawn for this proposal.

Research on Mobile Phones and Homelessness

Recent research is finding that significant numbers of homeless individuals, far from being completely cut off from technology, have access to mobile phones. A 2009 survey of unsheltered homeless individuals in Philadelphia found that 44 percent had mobile phones and 9 percent used them to access the Internet in the past 30 days.3 In the same year, a survey of homeless youth in Los Angeles found that 62 percent owned a mobile phone.4 More recently, a 2012 survey of home-less veterans in Massachusetts found that 89 percent had a mobile phone, with 32 percent possessing a smartphone.5 In addition to studying mobile phone prevalence among particular segments of the homeless population, researchers have also identified potential benefits of their use, including connecting with family and friends, fostering identity, and accessing informa-tion about employment and services.6

While the homeless increasingly have access to mobile phones, barriers still prevent reliable access to all of their advantages. Smartphones with reliable Inter-net access may be relatively rare among the homeless, and service disruptions due to an inability to pay are common.7 Free smartphones for the homeless with full service plans, as this project proposes, would overcome

some of the major barriers to maintaining a smart-phone, including cost and logistical problems with pay-ing bills. These benefits may be sufficient to incentivize individuals to upload regular information about them-selves. For the growing number of homeless individuals who already possess smartphones with reliable service plans, other incentives for uploading personal informa-tion could be used.

Other Mobile Phone Initiatives

Along with the wave of recent research on mobile phones and homelessness, new initiatives have begun providing mobile technology to homeless individuals:

• MOBILE4ALL. Mobile4All is an initiative launched by the Community Technology Alliance in partnership with Sparrow Mobile to provide free smartphones to homeless and low-income individuals. Service plan costs are generally the responsibility of recipients, although plans may be subsidized. The goal of the initiative is to use digital connectivity to help homeless individuals connect with housing, employment, and family. Mobile4All was launched in Santa Clara County, California, on October 30, 2014.8

• CONNECT 4 LIFE. Connect 4 Life is an ini-tiative of the LGBT Technology Partnership and Institute that provides homeless LGBT youth with free smartphones and service plans. The goal of Connect 4 Life is to put phones in the hands of homeless youth, presumably helping them con-nect with family, service providers, and employ-ment opportunities.9 The initiative was launched in Washington, DC, on March 4, 2015.

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• LINK-SF. Link-SF is an initiative developed by the St. Anthony Foundation, Zendesk, and Kim-berly McCollister. Rather than provide smart-phones to homeless individuals, Link-SF is simply a website optimized for mobile phones that pro-vides information about services to homeless and low-income individuals. It was launched in San Francisco on February 28, 2014.10

The major limitation of these projects is that they have not focused on in-depth data collection. This lim-its their potential for innovative research beyond the outcomes associated with possessing a smartphone or more easily accessing information about services. It also limits their potential to transform the homelessness sys-tem and homelessness policy more broadly.

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Collecting Data via Smartphones

To most effectively serve the homeless, we need vastly improved data. Currently, data collection

occurs at homeless shelters and other programs, but standards are inconsistent, data sharing between ser-vice providers can be difficult, and individuals are not regularly tracked while sleeping on the streets or after exiting homelessness. It is essential to track individuals throughout spells of homelessness and after, including the services they receive and the outcomes they achieve. Smartphones literally put data collection into the hands of the homeless and overcome the problems with rely-ing exclusively on service providers. At the same time, smartphones create new challenges.

Data Collection

For researchers and policymakers to collect data via smartphones, homeless individuals need uninterrupted access to smartphones and Internet plans. To this end, eligible individuals can be provided free basic smart-phones, optimally with low resale value to discour-age theft or sale. For example, the Motorola Moto E (Android operating system) retails for $119, and the Nokia Lumia 635 (Windows Mobile operating sys-tem) retails for $49. Refurbished models may be even cheaper.

As a condition of smartphone receipt, individu-als can be required to undergo training in how to use major smartphone features, complete an in-person sur-vey that asks for demographic and historical informa-tion, and consent to sharing data and participating in future randomized controlled trials. Individuals who already have smartphones could elect to receive mon-etary compensation to participate in the background survey. Full-service plans can be offered to individuals at no cost, but individuals may also elect to pay for and retain their personal service plan.

Aside from providing the capability to upload data via smartphones, it is necessary to create the incentives for individuals to actually do so. To this end, continued receipt of service plans can be conditioned on full com-pliance with instructions for uploading personal data. Noncompliance should result in service cancellations only after multiple attempts at resolution have been made. Even then, maintenance of limited voice and text plans should be considered, with Internet plans reinstated after required data are uploaded.11

Individuals who pay for personal service plans, and potentially those who receive free plans as well, could be offered compensation for regular data uploads, such as $0.50 for each daily upload and e-gift cards to restau-rants. If monetary incentives are used, assistance with opening online bank accounts should be offered during the initial survey.

Data uploads would be made via a custom-built smartphone app. Figure 1 displays hypothetical data entry screens. Any smartphone activity outside the app would not be tracked in order to avoid potential pri-vacy concerns. Push notifications would periodically notify individuals that a data upload is due and allow the user to open the app to answer required questions. Answering questions would generally rely on checking boxes rather than typing free form answers, and the app could rely on GPS when applicable. The amount of time required to complete questions would be min-imal. Questions received by a particular individual would be shaped by his or her characteristics, history, and personal privacy concerns. Topics of some poten-tial questions, grouped by the frequency at which they might be asked, are:

• One-Time Questions. Age, gender, race, home-lessness history, work history, last place of res-idence, date moved to current city, marital status, number of children, reliance on family and

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Figure 1. Hypothetical Data Entry Screens

friends, mental illness history, substance abuse history, employment history, and prior receipt of government assistance.

• Daily Questions. Sleep location, sleep location type (e.g., housing, shelter, outdoors, abandoned building, or hospital), contact with family or friends, subjective happiness, alcohol or substance use, physical health, and food consumption.

• Weekly Questions. Employment income, pub-lic assistance receipt, hours worked, job train-ing activities, mental health treatment, substance abuse treatment, and rating of service providers.

Data collection efforts should focus on single adults who experience homelessness for significant periods, particularly those who at least occasionally sleep on the streets. These individuals are the least well-understood and the most likely to sleep in places where data col-lection is not otherwise possible for sustained periods of time. This focus makes it feasible to fund data col-lection from individuals indefinitely, even outside of homelessness spells, and can advance our understand-ing of the factors that help some of the most vulnera-ble people remain housed. While this approach misses initial entries into homelessness, attempting to provide smartphones to all individuals at risk of homelessness would likely be cost prohibitive.12

200210

170

180$190

Federal City ShelterCovenant HouseThe Haven

Central Union Mission

Use current location

Other location

Daily Update

Updated Just Now

Where are you sleeping tonight?

Choose from recent shelters

Search or enter an address

DoneCancel

Weekly Update

Updated Just Now

DoneCancel

2122

181920 hrs

How many hours did you work?

How much did you earn?

KEVIN CORINTH

7

Challenges

One major challenge for this proposal is ensuring that uploaded data are accurate. Individuals might provide false data about themselves, or they could sell their phones and instruct buyers to answer the questions to maintain the free service plan. Several measures could mitigate these concerns. First, when uploaded infor-mation changes unpredictably, individuals could be contacted to seek explanation and verify that they still possess the phone. Second, changes in behavior within the app could be analyzed. For example, if the time it takes to upload data suddenly changes, this could be flagged for further investigation. Third, uploaded data could be corroborated with other data sources, such as shelter or hospital records. The pilot project can assess the extent to which false data reporting is a problem and whether these measures can effectively alleviate it.

Another extremely important concern is privacy. Individuals who experience street homelessness are more likely to represent vulnerable segments of the population, including those with severe mental illness

and substance abuse challenges. Furthermore, indi-viduals may prefer to keep their location and per-sonal information out of the hands of authorities such as police departments or other organizations. This of course makes data security extremely important. Any misuse or breach of data would not only have ethical ramifications but also damage the confidence of indi-viduals in the security of their data, thus reducing their willingness to participate. Privacy concerns will also help shape what types of questions are asked. Extreme care should be taken in designing any questions relat-ing to substance use and mental health symptoms. It may also be necessary to delay viewing location data for a significant period of time (e.g., a month) after it is uploaded to assure individuals that they will not be tracked in real time. This could mitigate fears that data would be used to arrest or otherwise contact individu-als without their consent. Ultimately, there is a tradeoff between data quality and privacy. The correct balance will require fully informed consent of people asked to upload data and approval from appropriate institu-tional review boards.

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Research Benefits

A major benefit of using smartphones for data col-lection is the ability to answer important ques-

tions unaddressed by previous research. This ability comes from collecting detailed, uninterrupted data on individuals over time—not just while in shelters or when they wind up in administrative records, but while they are on the streets and outside of homeless spells as well. Smartphones also provide an infrastructure for conducting randomized controlled trials, allowing us to deliver innovative interventions and automatically collect data as a condition of smartphone service plan receipt (or other incentives). This section first addresses what we know about homelessness—with a focus on individuals who sleep on the streets—and then dis-cusses what we can learn as a result of this proposal.

What Do We Currently Know?

Although limited, existing research provides import-ant insights into who experiences homelessness, its causes, its consequences, and the effectiveness of var-ious policies in alleviating it. Our current knowledge about homelessness (both unsheltered and sheltered) comes from several types of data:

• Community-level point-in-time counts of home-less populations,

• One-time surveys of homeless individuals,

• Nationally representative surveys that ask individ-uals about past experiences with homelessness,

• Administrative data from shelters and other pub-lic agencies, and

• Randomized controlled trials that test specific interventions targeted to the homeless.

Table A-1 in Appendix A displays the most com-mon data sources used in homelessness research bro-ken down by each of these types. Unfortunately, counts and surveys of the street homeless may miss many peo-ple and are otherwise unable to follow individuals over time.13 Records from shelters can track individuals over time, but not while they are sleeping on the streets or in housing. Other administrative records from hospitals and jails may not include information in high detail or with sufficient regularity.

Much of what we know about those who experi-ence street homelessness comes from an annual count required by the Department of Housing and Urban Development. On a given night in January 2015, 173,268 people were found sleeping in unsheltered locations across the country, with 52 percent of those found in California and Florida.14 Of unsheltered indi-viduals, 87 percent were adults experiencing home-lessness without children, 26 percent reported a severe mental illness, 25 percent reported a chronic substance abuse problem, and 9 percent were veterans.15 Mean-while, a 1996 survey based on in-depth interviews of users of homeless services who had been sleeping in unsheltered locations found:

• 44 percent had a mental health problem,

• 35 percent had a drug use problem,

• 45 percent had an alcohol use problem, and

• 75 percent had at least one of these three problems.16

KEVIN CORINTH

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Homeless spells are also longest for older males with alcohol and drug abuse histories, as well as the formerly incarcerated.17

Research on the causes of street homelessness is very limited given the difficulty of obtaining accurate counts.18 Nonetheless, studies generally find that total homeless population sizes are associated with higher apartment rents, worse macroeconomic conditions, lower apartment vacancy rates, more housing regu-lation, more shelters, and warmer weather.19 These studies rarely study street populations separately—if at all—given poor data quality. Studies based on individual-level data find that factors including pov-erty, mental illness, substance abuse, and incarceration are positively related to homelessness, although the vast majority of people with any of these characteristics do not become homeless.20 A 1991 survey conducted in New York City single-adult shelters found that individ-uals cite drugs, alcohol, and lack of employment as the primary causes of their homelessness.21

Research on the consequences of street homeless-ness is also limited due to a lack of quality data. Even research on sheltered homelessness does not gener-ally establish causal links between sleeping in shelters and health outcomes, but rather it documents correla-tions. Studies have documented high rates of tubercu-losis, hepatitis, HIV, repeated trauma, mental illness, and substance abuse among the homeless, as well as elevated mortality rates.22 In terms of costs to com-munities, a large body of evidence has found that the homeless—particularly those sleeping on the street—can be expensive to social services. The homeless use emergency rooms, jails, and homeless shelters at high rates and high costs.23 Studies have shown that these costs can be significantly curtailed among certain sub-populations who receive supportive housing, although the extent depends greatly on the region of the country and the particular individuals studied.24

Research on the effect of our policy responses to street homelessness is limited as well. One frequently debated policy response involves ordinances and police action to restrict activities often engaged in by homeless individuals or those serving them. These include restric-tions on sleeping in public areas, setting up encamp-ments, panhandling, and feeding programs for the

homeless. While the effects of such ordinances on the well-being of homeless individuals is unknown, their prevalence may be increasing.25 A second way com-munities deal with homelessness is by building shelters and other temporary services such as day-centers and soup kitchens. Studies based on New York City shel-ter censuses have suggested that higher shelter quality may increase the sheltered population.26 Cities also fear that such services will attract homeless individuals from other communities, but this has not been studied rigor-ously. A third policy response to homeless individuals, which has been studied more extensively, is the provi-sion of housing. Randomized experiments have shown that a “Housing First” approach—no-strings-attached permanent housing with supportive services—leads to improved housing stability among individuals dually diagnosed with mental illness and substance abuse, but mental health and substance abuse outcomes are not improved relative to a “treatment as usual” group.27

What Can We Learn?

Smartphones can revolutionize research on the unshel-tered homeless. The smartphone allows for continued data collection when individuals are not connecting with services—which may be the period of their high-est vulnerability. It also allows for much more detailed data collection when individuals are connecting with services, and for continued data collection after exits from homelessness. Furthermore, the smartphone can serve as a platform for conducting a wide range of ran-domized controlled trials. This does not mean smart-phones for the homeless should replace other data collection efforts. Surveys can achieve more representa-tive samples, and administrative records from shelters, hospitals, and jails can help corroborate self-reported data. Nonetheless, smartphones offer a unique oppor-tunity to answer a broad new set of questions.

Analysis of the data collected from smartphones can push forward our understanding of the people who experience street homelessness. Rather than relying on point-in-time surveys, we can create detailed, dynamic accounts of how people live and survive on the streets; how frequently they move among various unsheltered

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locations, shelters, and housing; how their circum-stances change over time; and their migration pat-terns across cities. We can also learn much more about why spells of homelessness begin and end—helping us understand the roles of family connection, social net-works, services, and employment. Research on the con-sequences of homelessness would be pushed forward as well. We could observe how sleeping in specific unshel-tered locations and how moving into and out of shel-ters and housing affect multiple dimensions of health (e.g., physical health, mental health, and substance use). Finally, data collected from smartphones would fill large voids in the literature on the effects of policies such as street sweeps, encampment closures, and city ordinances on the locational decisions and well-being of homeless individuals.

In addition to providing highly valuable data, smart-phones also provide an infrastructure for ongoing ran-domized controlled trials. Samples can be accessed at any time without any additional recruitment efforts, experi-mental interventions can be delivered directly via smart-phones, and no additional data collection is necessary. This infrastructure would make the marginal cost of a given randomized controlled trial equal to the cost of the intervention itself. This means that no-cost and low-cost interventions could be tried frequently and should lead

to a proliferation of experimentation. Experiments could evaluate existing policies, such as direct cash transfers, guaranteed shelter bed availability, free haircuts, and infor-mation about services. Innovative policies could be tested as well, including cash incentives for positive behaviors such as employment and service use, Uber credits sup-plied directly to smartphones, and paid work opportuni-ties that can be undertaken using smartphones.

Two important concerns specific to randomized con-trolled trials include ethical issues and spillover effects. Individuals should be instructed that randomized con-trolled trials will be conducted, and each randomized controlled trial should be reviewed by an institutional review board. Spillover effects are important because indi-viduals who receive a particular intervention as part of the treatment group may interact with members of the con-trol group, which could bias results. Parsing out the role of spillovers, such as through the use of sleeping location data, will be important.

Appendix B contains additional information about how using smartphones can push research forward. It describes a number of possible research questions that could be answered using the detailed data collected from smartphones as well as randomized controlled trials.

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Smarter Decisions and Accountability

While answers to new research questions can help inform policy improvements, the data we col-

lect can benefit the homeless in more direct ways as well. First, statistical algorithms would provide home-less individuals with real-time recommendations on major decisions. For example, they could recommend homeless assistance programs based on an individu-al’s characteristics, history, and preferences. Second, real-time evaluations of programs—based on statisti-cal techniques that track results and rank programs on their effectiveness—could be made publicly available. Private and public funders could determine which pro-grams are achieving the most effective results for the types of individuals they want to help. Not only would this drive more accountability among programs, but it may also increase total funding as donors gain a better understanding of the social impact of their investments.

Empowering the Homeless to Make Smarter Decisions

Homeless individuals could use uploaded data to make more informed decisions. Through a newly created smartphone app, this can be done in simple ways such as displaying an individual’s average historical health status when sleeping in different locations, or show-ing how happiness levels have changed through periods of work or greater connection with community. With constant access to personal data and an app that pres-ents data in simple and meaningful ways, individuals can be empowered with high quality information.

Furthermore, statistical algorithms can make per-sonalized recommendations regarding major decisions. Using the app, a particular individual could choose outcomes—such as housing stability, health, family connection, and employment—that are important to

him or her. Then, the app would process data from individuals with similar characteristics who undertook different courses of action to predict which decision is optimal. For example, the app could recommend ser-vice providers that are best matched to a particular indi-vidual’s goals and circumstances. It could even allow an individual to immediately apply or express a pref-erence for a particular service provider, depending on how program spots are allocated.28 Figure 2 displays a hypothetical program recommendation screen within the app.

Find Programs

Updated Just Now

ApplyNext Program

Recommended ProgramOnward DC

Housing

85% chance within one year

Predicted Earnings

$500/MONTH

HIGH CHANCE

within one year

Find Programs

Updated Just Now

ApplyNext Program


Housing


Predicted Earnings

$500/MONTH

HIGH CHANCE

within one year

Find Programs

Updated Just Now

ApplyNext Program


Housing


Predicted Earnings

$500/month

High Chance

within one year

Figure 2. Hypothetical Program Recommendation Screen

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Improving Accountability of Service Providers

The data collected from homeless individuals can also be used to make service providers more accountable for results. Long-term outcomes achieved by homeless clients of programs, adjusted for the clients’ character-istics and histories, can be used to rank programs by their overall effectiveness. A publicly available website could allow potential funders to select the outcomes they value and the types of individuals they prioritize

to generate customized performance measures and rankings. This information can drive smarter deci-sions about where to allocate their limited resources and may even encourage additional giving. Moreover, service providers could monitor their own effectiveness on an array of outcomes, as well as how they compare with other service providers serving similar types of individuals. This would enable them to better identify their relative strengths and weaknesses, and potentially improve practices.

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Homeless Innovation Center

In the long run, smartphones can also transform pol-icy to provide personalized interventions to individ-

uals that reflect changing circumstances and evolving evidence. In particular, a Homeless Innovation Center could be funded and authorized not only to conduct research and conduct randomized controlled trials, but also to simultaneously use the evidence to dynamically craft personalized packages of interventions for home-less individuals. Pairing dynamic research with the power to develop interventions free of bureaucratic hur-dles could lead to unprecedented innovation in home-less services. This section describes how the Homeless Innovation Center might be created, and how it could simultaneously generate evidence and implement per-sonalized interventions.

Creating a Homeless Innovation Center

The Homeless Innovation Center could be funded by multiple levels of government and/or private donors. Its major tasks would include:

• Managing the data collected from smartphones,

• Developing algorithms for assisting the home-less in making decisions and evaluating service providers,

• Developing and implementing a policy-centric research agenda including data analysis and ran-domized controlled trials,

• Dynamically incorporating evidence into indi-vidualized packages of interventions for homeless individuals, and

• Implementing dynamic interventions.

There could be either a single federal Home-less Innovation Center, a number of local centers, or potentially even privately funded centers without gov-ernmental authority. If multiple centers exist, close col-laboration should be highly encouraged. Optimally, participating government bodies should provide all homelessness-related funding to the Homeless Innova-tion Center.

It is essential that the center employ highly skilled individuals, including homelessness and mental health experts, data scientists, app developers, managers, a full research staff, and individuals with lived experiences with street homelessness. The center should pursue academic and private-sector partnerships (particularly with tech companies) as well to incorporate the best thinking into research and the implementation of new interventions. The leading government agency (e.g., the federal government) could appoint a director, who would be held accountable for the center’s work. The director could be instructed how to allocate resources across various subpopulations of homeless individuals and be provided with broad policy goals. However, spe-cific interventions should be left to the discretion of the director and staff in order to maximize innovation and efficiency.

Generating Evidence and Implementing Interventions

The operations of the Homeless Innovation Center would revolve around evidence generation and the imple-mentation of interventions. Data collected from indi-viduals via smartphones would be analyzed to determine factors that predict positive outcomes, and randomized controlled trials would be conducted to determine the causal impacts of innovative interventions. As evidence

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forms about which interventions work best for different people, unique packages of interventions can be crafted for each individual, then refined as the body of evidence continues to grow. For example, a particular package of interventions might include deployment of outreach workers using GPS on the smartphone (with consent), a referral to a highly rated service provider, and cash incentives for engaging with that provider. These inter-ventions would be selected based on outcomes achieved by similar individuals and evidence from randomized controlled trials. The package of interventions would be updated over time as the individual’s needs and circum-stances changed.

Marrying the authority to implement interventions with cutting-edge social science research would finally unleash true innovation in social services. The abil-ity to conduct iterative randomized controlled trials and other big data analysis would greatly accelerate the rate of scientific understanding about homeless-ness and what works. This scientific knowledge would then be instantly fed back into actual interventions, which would be refined and improved even more with further investigation and experimentation. Ulti-mately, a level of innovation typically reserved for the most forward-looking tech companies could be used to revolutionize services for the homeless sleeping on our streets.

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The Pilot

The ability of this entire proposal to work relies on a central but untested assumption: that homeless

individuals will upload accurate, uninterrupted data via smartphones. A pilot project should be undertaken to discover whether and how this can work. The design and potential funding sources for a pilot are discussed in this section.

Pilot Design

A pilot should be carried out by a coalition of partners in a single city. A research organization or academic institution should partner with local homeless service providers, an app development firm, and potentially a tech firm with data science expertise. Moreover, given that successful implementation of both the pilot and fuller project is literally in the hands of the homeless, individuals with lived experience with street homeless-ness should be central partners as well. They can offer insights into how to efficiently distribute smartphones, what practical considerations may interfere with pro-gram goals, and more generally what they and others might want out of the project.

The pilot’s basic objectives include creating and refining a data collection app, developing best practices for smartphone distribution, and understanding the incentives necessary to obtain reliable data from home-less individuals. The data collection app should be as simple and intuitive as possible. It should be informed by input from all partners and refined based on actual use. Similarly, smartphone distribution practices should be developed carefully, with training procedures for smartphone and app use continually evaluated and improved. Experimentation with incentives for upload-ing data should be undertaken as well. Various combi-nations of full service plan provision along with cash

and other in-kind incentives can be tested.To most effectively advance these basic objectives,

the pilot should include a strong focus on both qualita-tive and quantitative research. On-the-ground observa-tion and semi-structured interviews with smartphone users can help detect and overcome barriers to main-taining smartphones and uploading data. At the same time, quantitative analysis of data uploaded via the app can help detect data quality issues and potential inac-curacies. Measuring participation rates across individ-uals and over time, and how participation is associated with factors such as sleeping locations can suggest areas where improvements are needed.

The scale of the project should depend on fund-ing availability, but the project would likely require a two-year commitment and at least 200–300 smart-phones. Significant allowance should be made for lost, stolen, or sold smartphones; unforeseen research and app development costs; and the cost of monetary and in-kind incentives. While the scale of the pilot should remain manageable, it should optimally be given the flexibility to increase scale in order to most quickly learn how to efficiently collect data.

Funding

The major costs of a pilot are driven by smartphones, service plans, app development, and staff. Fortunately, fully capable smartphones can be relatively cheap (around $100 or less), and using refurbished mod-els can cut costs even further. It is important that the smartphones have a low resale value relative to the value of ongoing service plans in order to limit the resale or theft of phones. Service plans are more expensive than the smartphones themselves and may cost approxi-mately $30 per month for voice, text, and Internet.29

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Offering free voice and text along with Internet service provides an additional incentive for homeless individu-als to continue to participate in the project; it also alle-viates the need to carry multiple phones. App design and development should be contracted out to a highly capable firm with a longstanding reputation for creat-ing apps that are simple, intuitive, and able to process large amounts of data.

A back-of-the-envelope calculation places the overall cost at just under $350,000, based on 300 smartphones with two-year service plans and $100,000 allocated to app development.30 However, research and project management staff would be costly, as would potential cash incentives used to induce people to upload per-sonal data. These elements could drive up the cost of the pilot substantially.

There are different potential strategies for funding a pilot project. Perhaps the simplest would be for a pri-vate company or foundation to provide all necessary funding. A tech company with expertise in data sci-ence may be particularly well-suited. Partnerships with mobile companies could be explored to help subsidize the cost of phones and service plans. Smaller individual donations could be solicited as well, potentially using crowd-sourcing mechanisms.

Another alternative is government funding. The Department of Housing and Urban Development (HUD) spent $44 million in 2014 to subsidize data col-lection in homeless assistance programs.31 Meanwhile, HUD spends millions of dollars on HUD-sponsored

research projects on homelessness each year.32 With its potential to transform both data collection and research, this project could be funded through either of these means.

Alternatively, the federal Lifeline program, which subsidizes phone service for low-income Ameri-cans, could be tapped for a pilot and potentially the larger-scale project as well. The Lifeline program is funded by consumers of phone service, with spend-ing totaling $1.7 billion in 2014.33 Due to con-cerns about fraud and poor targeting, and a growing interest in incorporating broadband service, reforms of the program are being considered. In 2012, the Federal Communications Commission authorized up to $25 million for pilot programs to test new service models incorporating broadband services.34

The pilot project described here could be funded in part as a new Federal Communications Commis-sion pilot project. The homeless sleeping on the streets are some of the most disconnected members of soci-ety, and connecting them with smartphones and ser-vice plans could provide substantial social value. At the same time, however, Lifeline’s goal of provid-ing communications service to people who could not otherwise afford it is less aligned with the pilot’s overall goal of learning how to collect data from users. Thus, for the pilot and fuller implementation of the proposal, the Lifeline program could be an import-ant, but likely only partial, source of funding.

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Conclusion

Smartphones can help bring the big data revolution to some of the most vulnerable members of our

society. The potential benefits are substantial. Unprec-edented data collection and opportunities for ran-domized controlled trials could mean a renaissance for homelessness research. Equipping the homeless with recommendations powered by big data would enable more informed decision making. Making pro-gram performance data available to funders would mean more program accountability to private and

public funding sources. A Homeless Innovation Center simultaneously charged with generating evidence on what works and provided the authority to implement interventions could unleash unprecedented innova-tion in homeless services. As these goals are ambitious, a pilot project is necessary. Its goal is to understand how to most efficiently and accurately collect data from homeless individuals using smartphones. If quality data collection really is possible, a revolution in homeless services could very well follow.

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Type Name/Study Year(s) Sample Key Information

Homeless Counts HUD Count 1983 Sheltered homeless in select areas

Counts in major cities only

Decennial Census

1990–2010 All homeless people Counts of sheltered and street homeless

HUD Point-in-Time Counts

2005–2015 All homeless people Community-level counts conducted during January

Homeless Surveys

Urban Institute Survey

1987 National sample of homeless service users in large cities

First major national survey of homeless

National Survey of Homeless Assistance Providers and Clients

1996 National sample of homeless service users

Highly detailed survey

National Surveys Collaborative Psychiatric Epidemiology Surveys

2005 National sample over-sampling vulnerable populations

Does not distinguish between forms of homelessness

Fragile Families Survey and Child Wellbeing Study

1998–2009 National sample of children born 1998–2000

Follows children and families over time

Administrative Data

Homeless Management Information Systems (HMIS)

Varies All users of HMIS-compliant homeless services

Person identifiers allow links to other administrative data

Randomized Controlled Trials

Varies Varies Varies; focus on homeless with mental illness and substance abuse problems

Effectiveness of particular interventions

Table A-1. Major Homelessness Data Sources

Appendix A

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Appendix B

Research Questions based on Data Collected from Smartphones

1. How does sleeping location and time outdoors affect health, hospital use, and mortality?

The effect of homelessness on health is among the most studied questions among scholars, and is one of the primary motivations for interventions that help transition people into housing. But studies have been limited by an inability to distinguish between types of sleeping locations, including whether individuals are sheltered or sleeping on the streets. Without access to detailed, longitudinal data, causal links are difficult to establish.

With the ability to track nightly sleeping locations and health outcomes, we can make substantial prog-ress in understanding the health effects of different forms of homelessness. Moreover, exogenous weather shocks and policies that move people into shelters or housing programs can help establish causal links between homelessness and health. For example, many cities (e.g., Washington, DC) institute a “hypothermia alert” in which outreach teams actively recruit people from the street into shelters when the temperature falls below a particular threshold. A regression disconti-nuity approach would allow us to identify the causal effect of shelter use on health for temperatures around the hypothermia threshold.

2. How does weather affect sleeping location and health?

One of the foremost concerns about homelessness is the physical harm it can inflict on those sleeping on the streets, especially due to severe weather. However, little is known about how different forms of severe weather affect the health of individuals, how individu-als cope with severe weather events, and whether vari-ous coping strategies are effective. Using data on events such as cold spells and rainstorms, we can determine how weather affects sleeping locations, service use, and health outcomes.

3. What is the pattern of sleeping locations for individuals?

While point-in-time counts provide a snapshot of where people sleep on a given night, they do not tell us where those same people were the night, week, or month before. With the ability to track sleeping loca-tions nightly, we can determine how frequently people change locations, how far they travel, and how their location depends on the time of year. This informa-tion could inform street outreach efforts that attempt to convince homeless individuals to connect with services.

4. How does co-location on the streets relate to outcomes?

People sleeping on the street often learn essential survival skills, one of which is sleeping in a group. How co-location affects outcomes and service use has not been studied, however. With either sufficient smart-phone use by the homeless in a city or supplemental questions about co-location, we could study the effect of this survival tactic on health, service use, and other outcomes.

5. What effects do street sweeps have on homeless individuals?

City officials frequently conduct street sweeps in which the belongings of homeless individuals are removed and either thrown away or temporarily stored elsewhere. It is unknown what effect this has on sleeping locations and outcomes such as finding work or connecting with services. Street sweeps could be identified either from public announcements, cooperation with city officials, or accounts from the homeless themselves reported via their smartphones.

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6. What effects do arrests have on homeless individuals?

The homeless who sleep on the streets have fre-quent involvement with law enforcement—typically for minor offenses. Arrests are very expensive and disliked by police officers as well as by the individu-als being arrested. Using either self-reported arrests or public records, we could determine what effects arrests have on future sleeping locations, service use, and other outcomes.

7. How do ordinances that make street homeless-ness more difficult affect sleeping locations and outcomes?

Cities enact various types of ordinances that can make living on the streets more difficult. Ordinances include restrictions on outdoor feeding programs, bans on sleeping in particular areas, and bans on panhan-dling. Advocates for the homeless persistently shame the cities that enact these types of measures, and the media is no more forgiving. Yet cities continue to enact these laws, with some even claiming they benefit the homeless. We could provide evidence on precisely this question: How do new ordinances affect the sleeping locations and other outcomes of homeless individuals? Do they push people into services, out of town, or fail to move them at all?

8. Do new homeless shelters attract homeless people from other areas?

When cities find growing numbers of homeless peo-ple on the street, an argument is often made for new homeless shelters. But others fear that doing so will attract more homeless people to the area. By identify-ing the construction of new shelters, we could deter-mine whether homeless people from surrounding areas are attracted to the shelter itself or its vicinity.

9. How do outcomes vary over the course of a cal-endar month?

Public assistance benefits generally are made avail-able at the beginning of a month. Significant numbers of homeless individuals receive assistance from pro-grams including Supplemental Security Income, Social Security Disability Insurance, and the Supplemental

Nutrition Assistance Program (food stamps). Exam-ining location, service use, and health outcomes over the course of the month could help determine whether these programs have beneficial effects on recipients.

10. Are homeless encampments beneficial for the homeless?

Homeless encampments are in a constant flux of being opened by the homeless and shut down by gov-ernment officials. Officials typically argue that encamp-ments are harmful for the people who reside there, while homeless advocates argue that shutting them down further disrupts the lives of those displaced. New camps often pop up nearby. Using location data pro-vided by the homeless, we could study movement and outcomes that result from shutting down homeless encampments. Whether people move out of town or into services, and whether outcomes such as health are made worse, would provide important evidence on the efficacy of homeless encampments.

11. What factors are associated with people exiting the streets?

Individuals often transition between sleeping on the streets and shelters. While weather likely plays a central role, other factors such as work activity and connec-tions made with others may also be important.

12. What factors are associated with people remain-ing out of homelessness?

Nobody stays homeless forever, but even when people exit homelessness, many return in the future. Because we have been unable to track people when they leave the homelessness system, we cannot deter-mine which factors are associated with staying out of homelessness. But smartphones allow us to track peo-ple regardless of service use, allowing us to track which activities and connections are important for staying housed and a range of other outcomes such as health and happiness.

13. What factors are associated with people obtain-ing and maintaining employment?

Being homeless, and especially sleeping on the streets, can make obtaining and maintaining employment

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very difficult. Some individuals are nonetheless able to work. Tracking work activity along with factors such as health, service use, and sleeping location could provide clues into what obstacles stand in the way of sustained employment.

14. What are the patterns of and factors associated with homeless migration?

Homeless migration across cities is extremely important for policy. Substantial migration—or the perception of it—can motivate cities to underpro-vide to their homeless population in hopes that they will move on. However, we have only limited informa-tion about what causes homeless individuals to move between cities, how frequently they do so, and how migration relates to the seasons. By tracking sleeping locations, we can follow migration patterns over time, as well as determine the factors (e.g., job loss, weather changes, and changes in service provision) that precede moves.

15. Where do people sleep on major holidays (e.g., Thanksgiving and Christmas), and do holidays have lasting effects on outcomes?

Churches, charities, and the media pay more atten-tion to homelessness during the Christmas season than any other time of the year. At the same time, families may be most likely to reconnect—at least temporar-ily—with their homeless family members during this time. The extent to which homeless individuals use the greater availability of services and reconnect with fam-ily is unknown. By tracking nightly sleeping locations and service use, we could answer these questions, as well as determine what effect the holiday season has on other outcomes such as health, happiness, and housing stability.

Randomized Controlled Trials

1. How does information about services affect ser-vice use and outcomes?

Experts sometimes advise people to offer informa-tion on services such as shelter and soup kitchens to homeless individuals they encounter on the street.

Moreover, some social entrepreneurs are developing apps and websites that provide more accessible infor-mation about shelters and other services. It is unclear whether such information is useful. An experiment could provide information about services to a treatment group and pay them to correctly finish a quiz afterward to ensure receipt of information. We could then track how service use and other outcomes are affected.

2. How does information about weather affect shel-ter use?

A major concern about street homelessness is its hazard to health, especially when temperatures drop below freezing. While some studies have attempted to study the health effects of street homelessness, we do not know what policies most effectively get people off the streets and how such policies affect health. A series of interventions that provided information about weather alerts could help determine the best ways to get people into shelter and what effect this has on health outcomes.

3. How does information about the negative impacts of sleeping on the streets affect outcomes?

A large body of research examines some of the major negative consequences of homelessness. An open ques-tion is why individuals continue to sleep on the streets despite the presumably dangerous effects. One poten-tial explanation is that individuals do not realize the harm from doing so. An experiment could provide per-sonalized assessments of the dangers of street living to a set of individuals and determine what effect that has on sleeping locations and outcomes. Various ways of pre-senting the information could also be tested.

4. How does shelter use affect outcomes?It is considered a puzzle by some why homeless

individuals choose not to use services when they are available. Is it because they are uninterested in services altogether, or is it that most available services are inef-fective? An experiment could offer a treatment group cash conditional on shelter use with a control group provided cash with no strings attached. Short-term out-comes such as happiness, health, and future service use could be tested.

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5. How would a guaranteed shelter bed affect utilization?

A small number of communities offer all homeless individuals a legal right to shelter—either throughout the year as in New York City and Massachusetts or when the temperature is low enough as in Washington, DC, and some other cities. These laws are controver-sial because they arguably lead to higher shelter use at great public expense. Cities with such laws often fight to overturn them in court, arguing that a right to shel-ter is an inefficient drain on resources. It is unknown, however, how a right to shelter affects shelter use and outcomes for individuals. An experiment could provide a treatment group with a guaranteed shelter bed for a specified amount of time and could test various types of accommodation, from a cot on the floor to a pri-vate room, with different rules about sobriety, check-in times, and other standards of behavior. We could deter-mine how these interventions affect shelter use and outcomes, and thus provide evidence about the effec-tiveness of right-to-shelter laws.

6. How do various residential programs for the homeless affect outcomes?

On the basis of a limited number of experiments comparing a Housing First intervention with the sta-tus quo, the federal government has prioritized this service model across the country in its funding deci-sions. Other service models, however, have not been evaluated individually. An experiment could randomly assign individuals to various programs with different service models to test their effectiveness on a wide range of outcomes over extended periods of time. This evi-dence would be extremely important for future fund-ing decisions at all levels of government and among private donors.

7. How does cash affect outcomes?A frequently debated question that plagues the con-

sciences of many is whether giving cash to homeless individuals is beneficial to them. San Francisco histor-ically provided monthly cash grants to homeless indi-viduals but stopped the program in 2002 due to claims that it was detrimental to their health. At the federal level, many disabled homeless individuals qualify for

Supplemental Security Income, which offers monthly cash assistance.

Whether cash is an effective means of serving home-less individuals is an open question. An experiment could provide small amounts of cash to homeless indi-viduals (via electronic transfer or a remote pickup loca-tion). We could observe how this affects outcomes such as service use, future work efforts, and connection with family or friend networks.

8. How do cash incentives affect outcomes?We have no evidence on how cash incentives for

engaging in various positive behaviors affect out-comes for the homeless. Given that most services are extremely expensive, however, they could be a much cheaper way of achieving similar or superior outcomes. A set of experiments could offer cash incentives of var-ious amounts for different types of verifiable activities, such as checking in with service providers, applying for work, connecting with family, or staying off the street. Cash incentives could be supplied either in person or through electronic banking accounts.

9. How do in-kind donations such as clean socks, sleeping bags, or umbrellas affect outcomes?

Many organizations distribute in-kind goods to pro-vide immediate relief to homeless individuals, but crit-ics often argue that such programs actually harm the individuals who receive them. An experiment could provide free items to be delivered to individuals. Deliv-ery could be carried out by couriers using GPS locations on smartphones and could even be weather dependent. For example, umbrellas could be delivered before an impending rainstorm, and coats could be delivered before an impending cold spell.

10. How would free transportation affect outcomes?The homeless are often thought to congregate in

major urban areas due in part to transportation net-works. While shelters sometimes provide transporta-tion assistance to homeless clients, it is unknown what effect this has on outcomes. An experiment could pro-vide free monthly passes for public transit to individuals or supply pre-paid Uber rides directly via smartphones. Evidence on how subsidized transportation affects

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outcomes such as employment, family connection, and service use would be extremely useful for policymakers and nonprofit service providers.

A related experiment could test how subsidies for long-haul travel to other cities and states affect out-comes. Many cities provide subsidized travel to home-less individuals with family in other towns, but such assistance can be controversial if a city appears to be dumping its homeless population on other cities. How such subsidies are used and whether individuals return to their origin destinations would be important evi-dence in this debate.

11. How do humanizing services such as haircuts and showers affect outcomes?

A popular form of charity to the homeless is to provide humanizing services such as free haircuts and showers. While these services almost surely offer some amount of dignity, their effect on measurable outcomes is unknown. Given the low cost of such interventions,

their effectiveness in promoting any meaningful out-comes could be very influential for policy. An exper-iment could provide offers to individuals via their smartphones for these types of services, and outcomes could be tracked.

12. How does family connection influence housing and other outcomes?

Homelessness can usually be prevented by strong ties with family and community who will provide emergency resources, shared accommodations, and other support. Peter Rossi, in his seminal 1989 book, Down and Out in America: The Origins of Homeless-ness, proposes a subsidy for households who take in family members who are homeless. In an experiment, we could ask homeless individuals to provide contact information for family members, who would then be asked (or paid) to contact the individual or potentially take them in.

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About the Author

Kevin C. Corinth is a research fellow in economic policy studies at the American Enterprise Institute.

Acknowledgements

A number of individuals have provided invaluable support and feedback. I particularly thank Allan Baez, Olivier Bal-lou, Matthew Bauer, Nikolai Boboshko, Patrick Brown, Thomas Byrne, Elizabeth Corinth, Jewel Dauletalina, Rob-ert Doar, William Evans, Kathleen Ferreira, Newt Gingrich, Kevin Hassett, Matt Jensen, Jerry Jones, Sean Kennedy, Marina Martin, Aparna Mathur, Brendan O’Flaherty, Jeff Olivet, Thomas O’Toole, Lisa Pape, David Sanford, Anna Scherbina, Darin Selnick, Andrew Smith, Michael Strain, Stephen Taylor, Lisa Thomas, Amy Tucker, Stan Veuger, Alan Viard, Ross Worthington, and participants in the AEI Digital Governance Working Group. All errors and opinions are my own.

25

Notes

1. US Department of Housing and Urban Development, “CoC Homeless Assistance Programs: Homeless Populations and

Subpopulations,” February 3, 2016, https://www.hudexchange.info/manage-a-program/coc-homeless-populations-and-

subpopulations-reports/.

2. US Interagency Council on Homelessness, “The President’s 2016 Budget: Fact Sheet on Homelessness Assistance,” https://

www.usich.gov/tools-for-action/presidents-2016-budget-fact-sheet-on-homelessness-assistance.

3. Karin M. Eyrich-Garg, “Mobile Phone Technology: A New Paradigm for the Prevention, Treatment, and Research of the

Non-Sheltered ‘Street’ Homeless?” Journal of Urban Health 87, no. 3 (2010).

4. Eric Rice, Alex Lee, and Sean Taitt, “Cell Phone Use Among Homeless Youth: Potential for New Health Interventions and

Research,” Journal of Urban Health 88, no. 6 (2011).

5. D. Keith McInnes et al., “The Potential for Health-Related Uses of Mobile Phones and Internet with Homeless Veterans:

Results from a Multisite Survey,” Telemedicine and e-Health 20, no. 9 (September 2014).

6. Adrien Sala and Javier Mignone, “The Benefits of Information Communication Technology Use by the Homeless: A Narra-

tive Synthesis Review,” Journal of Social Distress and the Homeless 23, no. 1 (2014).

7. D. Keith McInnes et al., “The Potential for Health-Related Uses of Mobile Phones and Internet with Homeless Veterans:

Results from a Multisite Survey,” Telemedicine and e-Health 20 (2014).

8. Sparrow, “Mobile for All,” https://sparrowmobile.com/#mission.

9. Andrea M. Hackl, “Helping Homeless Youth Stay Connected: LGBT Tech Connect 4 Life Program and Research,” LGBT

Technology Partnership and Institute, 2015, http://lgbttechpartnership.org/lgbt-tech-releases-new-research-helping-homeless-

youth-stay-connected-the-connect-4-life-program/.

10. Link-SF, website, http://www.link-sf.com/.

11. For example, while data plans are suspended, post-deadline uploads could be made via Wi-Fi. Alternatively, it may be possible

to restrict smartphone data for all uses except data uploads via the app during periods of noncompliance.

12. Alternatively, a random sample of individuals at risk of homelessness could be selected to provide data, which would be much

more informative about the effects of various policies and interventions on homeless population sizes. However, the necessary sam-

ple size to study individuals who are currently homeless would be much larger and thus cost much more. Moreover, system-wide

and broader policy benefits require eventually collecting data from all individuals experiencing sustained spells of homelessness.

13. Some communities advertise their counts and surveys to homeless people ahead of time, although other communities do not

for fear that homeless individuals will avoid being outside or in areas likely to be canvassed by counters. Some communities also use

police officers to keep counters safe; however, this may also lead homeless individuals to hide from counters, especially for those with

previous negative interactions with law enforcement. US Department of Housing and Urban Development, “HUD’s Homeless

Assistance Programs: A Guide to Counting Unsheltered Homeless People,” 2nd revision, January 2008, https://www.hudexchange.

info/resources/documents/counting_unsheltered.pdf.

14. US Department of Housing and Urban Development, “CoC Homeless Assistance Programs.” Counts are likely higher in

warm-weather states in part because HUD Point-in-Time counts are conducted during the winter.

15. Ibid.

16. Martha R. Burt et al., “Homelessness: Programs and the People They Serve: Findings of the National Survey of Homeless

Assistance Providers and Clients,” Urban Institute, 1999, http://www.urban.org/research/publication/homelessness-

26


programs-and-people-they-serve-findings-national-survey-homeless-assistance-providers-and-clients.

17. Sam Allgood and Ronald S. Warren, “The Duration of Homelessness: Evidence from a National Survey,” Journal of Housing

Economics 12, no. 4 (December 2003): 273–90; and Randall Kuhn and Dennis P. Culhane, “Applying Cluster Analysis to Test a

Typology of Homelessness by Pattern of Shelter Utilization: Results from the Analysis of Administrative Data,” American Journal of

Community Psychology 26, no. 2 (April 1998): 207–32.

18. For a discussion of the problems with street counts, see Kevin C. Corinth, “Street Homelessness: A Disappearing Act?” AEI

Economic Perspectives, June 2015, https://www.aei.org/publication/street-homelessness-a-disappearing-act/.

19. Cecil Bohanon, “The Economic Correlates of Homelessness in Sixty Cities,” Social Science Quarterly 81, no. 4 (December

1991): 817–25; Marjorie Honig and Randall K. Filer, “Causes of Intercity Variation in Homelessness,” American Economic Review

83, no. 1 (March 1993): 248–55; Brendan O’Flaherty, Making Room: The Economics of Homelessness (Cambridge, MA: Har-

vard University Press, 1996); Paul W. Grimes and George A. Chressanthis, “Assessing the Effect of Rent Control on Homelessness,”

Journal of Urban Economics 41, no. 1 (January 1997): 23–37; Michael Cragg and Brendan O’Flaherty, “Do Homeless Shelter

Conditions Determine Shelter Population? The Case of the Dinkins Deluge,” Journal of Urban Economics 46, no. 3 (November

1999): 377–415; John M. Quigley, Steven Raphael, and Eugene Smolensky, “Homeless in America, Homeless in California,”

Review of Economics and Statistics 83, no. 1 (February 2001): 37–51; Dennis P. Culhane et al., “The Impact of Welfare Reform on

Public Shelter Utilization in Philadelphia: A Time-Series Analysis,” Cityscape: A Journal of Policy Development and Research 6, no.

2 (2003): 173–85; Brendan O’Flaherty and Ting Wu, “Fewer Subsidized Exits and a Recession: How New York City’s Family

Homeless Shelter Population Became Immense,” Journal of Housing Economics 15, no. 2 (June 2006); Brendan O’Flaherty and

Ting Wu, “Homeless Shelters for Single Adults: Why Does Their Population Change?” Social Service Review 82, no. 3 (September

2008): 511–50; Thomas Byrne et al., “The Relationship Between Community Investment in Permanent Supportive Housing and

Chronic Homelessness,” Social Service Review, no. 2 (June 2014): 234–63; Kevin Corinth, “Ending Homelessness: More Housing

or Fewer Shelters?” AEI Economic Policy Studies Working Paper Series, November 2015, http://www.aei.org/publication/

ending-homelessness-more-housing-or-fewer-shelters/.

20. Dirk W. Early, “The Determinants of Homelessness and the Targeting of Housing Assistance,” Journal of Urban Economics

55, no. 1 (January 2004): 195–214; Dirk W Early, “An Empirical Investigation of the Determinants of Street Homelessness,” Jour-

nal of Housing Economics 14, no. 1 (March 2005): 27–47; Greg A. Greenberg and Robert A. Rosenheck, “Mental Health Cor-

relates of Past Homelessness in the National Comorbidity Study Replication,” Journal of Health Care for the Poor and Underserved

21, no. 4 (November 2010): 1234–49.

21. New York City Commission on Homelessness, “The Road Home: A New Direction in Social Policy,” 1992.

22. Andrew R. Moss et al., “Tuberculosis in the Homeless,” American Journal of Respiratory and Critical Care Medicine 162,

no. 2 (August 2000): 460–64; Andrew Rosenblum et al., “Hepatitis C and Substance Use in a Sample of Homeless People in New

York City,” Journal of Addictive Diseases 20, no. 4 (January 2001): 15–25; Majorie J. Robertson et al., “HIV Seroprevalence

Among Homeless and Marginally Housed Adults in San Francisco,” American Journal of Public Health 94, no. 7 (July 2004):

1207–17; Aaron J. Levitt et al., “Health and Social Characteristics of Homeless Adults in Manhattan Who Were Chronically or Not

Chronically Unsheltered,” Psychiatric Services 60, no. 7 (July 2009): 978–81; Stephen Metraux et al., “The Impact of Shelter Use

and Housing Placement on Mortality Hazard for Unaccompanied Adults and Adults in Family Households Entering New York

City Shelters: 1990–2002,” Journal of Urban Health 88, no . 6 (December 2011): 1091–104; David S. Morrison, “Homelessness

as an Independent Risk Factor for Mortality: Results from a Retrospective Cohort Study,” International Journal of Epidemiology

38, no. 3 (June 2009): 877–83; Mimi M. Kim et al., “Assessing Trauma, Substance Abuse, and Mental Health in a Sample of

Homeless Men,” Health and Social Work 35, no. 1 (February 2010): 39–48.

23. Stephen R. Poulin et al., “Service Use and Costs for Persons Experiencing Chronic Homelessness in Philadelphia: A Popula-

tion-Based Study,” Psychiatric Services 61, no. 11 (November 2010): 1093–98; US Department of Housing and Urban Development,

“Costs Associated with First-Time Homelessness for Families and Individuals,” 2010; and Sharon A. Salit et al., Hospitalization Costs

Associated with Homelessness in New York City,” New England Journal of Medicine 338, no. 24 (June 11, 1998): 1734–40.

24. Dennis P. Culhane, “The Costs of Homelessness: A Perspective from the United States,” European Journal of Homelessness

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2, no. 1 (2008): 97–114; Dennis P. Culhane, Stephen Metraux, and Trevor Hadley, “Public Service Reductions Associated with

Placement of Homeless Persons with Severe Mental Illness in Supportive Housing,” Housing Policy Debates 13, no. 1 (2002):

107–63; and Mary E. Larimer, Daniel K. Malone, and Michelle D. Garner, “Health Care and Public Service Use and Costs Before

and After Provision of Housing for Chronically Homeless Persons with Severe Alcohol Problems,” JAMA 301, no. 13 (April 1,

2009): 1349–57.

25. National Coalition for the Homeless, “Share No More: The Criminalization of Efforts to Feed People in Need,” 2014;

National Law Center on Homelessness and Poverty, “Criminalizing Crisis: The Criminalization of Homelessness in U.S. Cities,”

2011; and Berkeley Law Policy Advocacy Clinic, “California’s New Vagrancy Laws: The Growing Enactment and Enforcement of

Anti-Homeless Laws in the Golden State,” University of California, Berkeley, 2015.

26. Michael Cragg and Brendan O’Flaherty, “Do Homeless Shelter Conditions Determine Shelter Population? The Case of the

Dinkins Deluge,” Journal of Urban Economics 46, no. 3 (November 1999): 377–415; and O’Flaherty and Wu, “Homeless Shelters

for Single Adults.”

27. Sam Tsemberis, Leyla Gulcur, and Maria Nakae, “Housing First, Consumer Choice, and Harm Reduction for Homeless

Individuals with a Dual Diagnosis,” American Journal of Public Health 94, no. 4 (April 2004): 651–56; Deborah K. Padgett, Leyla

Gulcur, and Sam Tsemberis, “Housing First Services for People Who Are Homeless with Co-Occuring Serious Mental Illness and

Substance Abuse,” Research on Social Work Practice 16, no. 1 (January 2006): 74–83; and Paula Goering et al., “National At

Home/Chez Soi Final Report,” Mental Health Commission of Canada, 2014.

28. Whether applications can be submitted directly would depend on intake procedures in a given community. Many commu-

nities have embraced “coordinated entry” approaches that use a centralized process for assigning individuals to programs. In this

context, the preference submitted by an individual could be one factor among others in making assignments.

29. This cost estimate is based on conversations with an industry source and from other initiatives providing smartphones to

homeless and low-income individuals.

30. This calculation assumes a cost of $100 per smartphone and $30 per month for each service plan.

31. “US Department of Housing and Urban Development, CoC Homeless Assistance Programs.”

32. For a list of projects, see US Department of Housing and Urban Development, Office of Policy Development and Research,

“HUD Research Roadmap FY 2014–2018,” 2013.

33. US Government Accountability Office, “Telecommunications: FCC Should Evaluate the Efficiency and Effectiveness of the

Lifeline Program,” GAO-15-335, March 2015, http://www.gao.gov/assets/670/669209.pdf.

34. Federal Communications Commission, “Wireline Competition Bureau Low-Income Broadband Pilot Program Staff

Report,” WC Docket No. 11-42, May 22, 2015, https://www.fcc.gov/document/wcb-low-income-broadband-

pilot-program-staff-report.

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